Network State Description via Cluster Attribute Statistics
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Solution Overview
Problem
Modern communications networks face challenges in providing a useful overview of their operational state due to the vast and varied management traffic, with existing data mining techniques like clustering often producing sets of points rather than interpretable patterns, making it difficult for administrators to understand the network's state effectively.
Innovation Solution
A method is developed to automatically generate a succinct description of the communications network's state based on network operational data, using attribute average and variability measures to label groups, allowing for efficient querying and management of network entities, thereby reducing computational resource demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If clustering algorithms are used to organize network operational data, then the data can be grouped into clusters, but the clusters are produced as sets of points rather than interpretable patterns
Solution Approach 1:
The patent transforms cluster representations from raw coordinate sets to parameter-based descriptions using statistical measures (mean, standard deviation, min, max) of network operational attributes. This parameter transformation makes clusters interpretable while preserving essential pattern information about network behavior.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts clustering results into human-readable descriptions. This intermediary translates abstract cluster coordinates into meaningful network state summaries using attribute statistics, bridging the gap between computational clustering and human interpretation.
2Measurement precision
If clustering is performed on vast amounts of network operational data, then comprehensive network state analysis is achieved, but great computational resources are required
Solution Approach 1:
The patent segments the network operational data into manageable clusters based on attribute similarities. By dividing the vast dataset into smaller cluster groups and analyzing each cluster's statistical properties rather than processing every individual data point, computational resources are significantly reduced while maintaining analysis accuracy.
Solution Approach 2:
The patent applies partial action by computing only the essential statistical parameters (mean, standard deviation, min, max) for each attribute in each cluster, rather than performing exhaustive analysis on all data points. This partial computation approach achieves sufficient measurement precision with reduced computational overhead.
3Loss of information
If cluster descriptions include detailed attribute ranges, then accurate representation of cluster contents is achieved, but the descriptions become less succinct
Solution Approach 1:
The patent uses statistical parameter transformations to represent attribute distributions concisely. Instead of listing all attribute values or detailed ranges, it computes mean, standard deviation, minimum, and maximum values that succinctly capture the essential characteristics of each attribute within a cluster, maintaining accuracy while improving brevity.
Data Source
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AI summary
A method of operating a communications network is disclosed. Modern communications networks produce vast amounts of network operational data which have the potential to provide a useful summary of the operational state of the network. Whilst processes such as clustering are known for arranging the vast amount of data into groups, the clusters themselves do not provide data which might be easily interpreted by network elements or administrators. Network operational data often comprises a plurality of data items, each of which gives a value for each of a set of attributes. By processing a cluster to identify attributes in the cluster whose values vary less in the cluster then they vary outside of the cluster, and then generating a cluster description which is based on a measure of the central tendency of the values of those attribute in the cluster, an easily interpretable general description of the data items in the cluster is provided. The easily interpretable general description of the cluster can then be used to relatively identify data items similar to those present in the cluster (e.g. from a larger database of data items), and elements in the network can then act autonomously on the basis of the cluster description to control the operation of the communications network.